This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Early detection of breast cancer will continue to be crucial in improving patient survival rates. Manual breast exams (breast palpation) and mammograms are currently the most effective and widely used techniques for early detection. Unfortunately, manual breast exams are limited in their ability to detect tumors reliably since they only produce local information about the site where the force is applied and do not provide quantitative measurements. Mammograms, while effective, expose the patient to radiation and hence routine mammograms are limited to those in specific age or risk groups. In addition, mammograms do not quantify tissue stiffness, an identifying characteristic of breast tumors. Our ultimate goal is to develop an Automated Palpation System (APS) which will be completely noninvasive and painless. We envision a table where the patient lays in a prone position in a manner that allows dozens of mechanical fingers to engage, gently manipulate, and measure the resulting response of the breast in order to obtain a force/deflection map of the breast surface. From this surface force/deflection map, an image of the tissue stiffness throughout the breast can be calculated. These recordable/quantitative images can be then be used to identify tumors because of their higher stiffness. This particular project focuses solely on developing efficient computational algorithms for the APS. When fully developed, the APS could be used as a relatively inexpensive and risk-free breast cancer screening mechanism.